The operation of wireless access networks for enabling wireless communication is highly energy consuming. In view of current environmental concerns, increased attention is paid recently to the energy consumption of telecommunications networks.
Various studies have been performed to reduce conventional energy consumption in wireless access networks, e.g. by exploring the option of using sustainable energy sources (Ericsson AB White Paper “Sustainable energy use in mobile communications”, June 2007).
With the development of the 3GGP Long Term Evolution (LTE) network, energy saving for the network is also approached in the context of self organizing networks (SONs). In a White Paper of NEC, dated February 2009, “NEC's proposals for next-generation radio network management”, energy is considered as a significant part of the operation expenses of a cellular network. It is recognized that the main saving potential resides in using variations in load over time, that allows to switch off parts of the resources, for example during the night. When a complete base station is switched off, other base stations of the access network need to compensate for the reduction in coverage area and capacity. This requires coordination between the nodes. A similar use case is described in 3GGP TR 36.902 v9.1.0 “Self-configuring and self-optimizing network (SON) use cases and solutions”.
The activation and deactivation of base stations, or cells thereof, or reduction of its operability has implications for user devices (terminals, user equipment (UE)) in the coverage area of these base stations or cells.
The current estimation when to switch off/on a base station (or cell) and which base stations to switch off/on is usually based on load and configuration information that might be complemented with handover (HO) statistics. Load measurements can e.g. be performed for one or more cells in the network of a network operator. The network operator also has detailed information on the configuration of e.g. the base station antenna directions and tilting, base stations transmit powers, etc. that can be used, with support of propagation models in order to estimate the best server areas per cell. Additionally, from network HO counters, the network operator can make HO statistics for the cells. In this way by combining configuration settings, propagation/planning data and HO statistics the network operator can estimate when a particular cell (or base station) can be switched off/on and which remaining cells may provide compensating coverage in the areas of the cells that have been switched off.
The current estimation techniques provide several disadvantages. The estimations are based on models for the antenna patterns and propagation conditions. These models have intrinsic inaccuracy when compared to the real-life situation due to modelling errors and simplifications. Additionally, any change in the antenna configuration, propagation environment, etc. has to be accurately and timely updated in order to maintain some accuracy of the estimation. This can be a rather demanding task, especially in case of self-optimizing radio access networks that dynamically reconfigure antenna set-up (e.g. tilting or azimuth), downlink transmission powers, etc.
Furthermore, the planning tool for estimation has to be run in parallel with the changes of the antenna configuration settings, downlink power settings, etc. in order to obtain up-to-date estimation. Running coverage/planning predictions is usually only executed off-line at network roll-out and network extensions. Running such predictions in parallel with the network operations and entirely consistent with any change to the network's configuration is a cumbersome task.
Still further, the estimation via planning/propagation tools and configuration data is usually based on some kind of ‘average user’ assumptions or predictions for the spatial distribution of the user devices and/or the traffic related to the devices. This is another source of intrinsic error due to the uncertainty of the predictions. Even though these spatial traffic/device distributions might realistically reflect the average traffic/device spatial distributions, the actual distributions might strongly deviate from the ‘average’ situation at the moment when a cell has to be switched off or on.
There exists a need in the art for improved control on the effect of the activation/deactivation of base stations, or cells thereof, on the user devices associated with these base stations.